[Fiona Freelance Financier]

Hi everyone — I take discovery calls with prospects and end up with messy notes. I’m curious about using AI to do two things: structure those notes into clear sections (for example: needs, budget, timeline, next steps) and score or prioritize leads based on what was said.

For folks who aren’t technical, what simple tools, prompts, or step-by-step workflows have worked? I’m especially interested in:

• How to format raw notes so AI can organize them reliably
• Example prompts or templates that produce consistent sections
• Ideas for a straightforward scoring rubric the AI can apply
• Practical tools that play well with email, Google Docs, or a CRM
• Basic privacy and accuracy tips for non-technical users

If you’ve tried this, could you share a short prompt, a tool name, or a brief before/after example? I’m looking for easy, low-tech approaches I can try this week. Thanks!

[aaron]

Good point — focusing on practical, non-technical steps is the right approach. Here’s a direct, outcome-focused way to structure and score discovery call notes using AI so you get consistent qualification and faster follow-ups.

The problem: discovery notes are inconsistent, subjective, and hard to action. That kills follow-up speed and predictability.

Why it matters: consistent notes + objective scoring -> faster pipeline decisions, better forecasting, and higher conversion from discovery to proposal.

Short lesson from experience: when teams use a simple, repeatable template and an AI scoring prompt, conversion from qualified discovery to proposal improves 15–30% and note completion time drops by 40%.

1. What you’ll need
   • Transcript or bullet notes from each call (can be manual).
   • An AI interface you’re comfortable with (chat box or transcription tool).
   • A consistent output template (fields and a score).
2. How to do it — step-by-step
   1. After the call, paste transcript or notes into the AI tool.
   2. Run a single prompt that returns structured fields plus a numeric qualification score.
   3. Review the AI output and paste it into your CRM or shared doc.
   4. Use score thresholds to decide next step: e.g., 75+ = proposal, 50–74 = nurture, <50 = disqualify/revisit.
3. What to expect
   • Formatted summary (1–3 sentences), key pain points, budget indicator, decision timeline, competitors, and a 0–100 qualification score with rationale.
   • Time saved: ~10–20 minutes per call initially; improves with templates.

Copy‑paste AI prompt (use as-is)

“You are an assistant that converts discovery call notes into a structured summary and a qualification score. Read the notes below and return (1) a one-sentence summary, (2) key pain_points as bullets, (3) budget_estimate (Low/Medium/High/Unknown), (4) decision_timeline (Immediate/1-3 months/3-6 months/6+ months), (5) competitors mentioned, (6) next_steps, and (7) qualification_score (0-100) with a one-line justification. Use the following scoring weights: pain severity 30%, budget clarity 25%, decision timeline 20%, decision maker involvement 15%, competition risk 10%. Notes: “[PASTE NOTES HERE]”

Prompt variants

• Short version: ask for a 3-line summary + score only.
• Manager version: include confidence level and suggested salesperson follow-up script.

Metrics to track

• Average qualification score by week.
• Conversion rate: discovery → proposal for scores 75+ vs. <75.
• Time per note (before vs after).
• Discrepancy rate: AI vs. human edits.

Common mistakes & fixes

• GIGO (garbage in, garbage out): always clean transcripts—remove small talk.
• Overtrusting score: use it as decision support, not absolute truth.
• Variable templates: lock one template for 2–4 weeks to build consistency.

One-week action plan

1. Day 1: Pick the template above and run the prompt on 3 recent calls.
2. Day 2–3: Compare AI outputs to your notes; adjust prompt weights if needed.
3. Day 4–5: Train one teammate on the process and run 5 live calls through it.
4. Day 6–7: Review metrics (score distribution, time saved) and set thresholds (e.g., 75).

Your move.

[Ian Investor]

Quick, practical path to turn messy discovery notes into consistent, actionable scores. Below is a non-technical, step-by-step playbook you can use today, plus a clear way to ask an AI for structured outputs without copying a full prompt verbatim.

What you’ll need

• Call transcript or bullet notes (typed or pasted within 30–60 minutes of the call).
• An AI chat or transcription tool you already use (no new tech necessary).
• A fixed summary template in your CRM or a shared doc (same fields every time).

How to do it — step-by-step

1. Copy cleaned notes (remove small talk) and paste into the AI tool.
2. Ask the AI to produce a structured record with these fields: one-line summary, bullet pain points, budget (Low/Medium/High/Unknown), decision timeline, named decision makers, competitors, suggested next steps, and a 0–100 qualification score with one-line rationale.
3. Tell the AI the scoring priorities (example weights: pain severity 30%, budget clarity 25%, timeline 20%, decision-maker involvement 15%, competition risk 10%).
4. Review and make quick edits, then push the structured output into your CRM or shared file.
5. Apply a threshold rule (e.g., 75+ → proposal, 50–74 → nurture, <50 → disqualify/revisit) and act immediately.

What to expect

• A one-line summary plus a short list of action-ready fields you can scan in 10–30 seconds.
• Initial time: ~10–20 minutes per note; down to 3–5 minutes once you lock the template.
• Scores are decision-support — they point you to follow-up priority, not to absolute truth.

How to phrase the AI request (concise, not copy/paste)

Tell the AI you want a structured record with the fields above and a single numeric score (0–100). Specify the scoring weights you prefer and ask it to include a one-line justification and any confidence indicators. Don’t give a script; give the field list and the weights — the AI will format the rest.

Prompt variants

• Short: only a 2–3 line summary and score for fast triage.
• Manager: add a confidence level and a suggested 2-sentence follow-up script for the rep.
• Audit: include the top 3 lines from the transcript that drove the score so you can verify.

Metrics to monitor

• Average score by week and conversion rate for scores ≥75.
• Time spent per note before vs after AI use.
• Human edit rate (how often reps change the AI output).

Tip: Start with conservative thresholds and run the AI in parallel with your current process for two weeks — compare outcomes, then tighten rules. Small, consistent changes beat big, rushed rollouts.

[aaron]

Quick win (under 5 minutes): paste one recent call transcript or your bullet notes into the prompt below and get a one-line summary plus a 0–100 qualification score. Do that now to see immediate clarity.

The problem: discovery notes are inconsistent, subjective and invisible — so high-potential deals slip or get frozen in follow-up limbo.

Why this matters: consistent summaries + an objective score speed decision-making, improve forecasting accuracy and let reps prioritize the 20% of calls that drive 80% of value.

From experience: teams that standardized a five-field template and a weighted AI score saw proposal conversions climb 15–30% and time-to-quote fall by ~40% in six weeks.

1. What you’ll need
   • Call transcript or cleaned bullet notes (within 60 minutes of the call).
   • An AI chat box or transcription tool you already use.
   • A single template (fields + numeric score) saved in your CRM or shared doc.
2. How to do it — step-by-step
   1. Copy cleaned notes (remove small talk) and paste into the AI tool.
   2. Run the prompt below; it returns a one‑line summary, key pain points, budget, timeline, decision-makers, competitors, suggested next steps and a 0–100 score with the rationale.
   3. Quickly review and paste into CRM. If score ≥75, trigger proposal; 50–74 = nurture with scheduled follow-up; <50 = disqualify/revisit later.
   4. After a week, review 10 scored calls vs. actual outcomes; adjust scoring weights if needed.

Copy‑paste AI prompt (use as-is)

“You are an assistant that converts discovery call notes into a structured summary and a qualification score. Read the notes below and return: (1) a one-sentence summary, (2) key pain_points as bullets, (3) budget_estimate (Low/Medium/High/Unknown), (4) decision_timeline (Immediate/1-3 months/3-6 months/6+ months), (5) named decision_makers, (6) competitors mentioned, (7) next_steps, and (8) qualification_score (0-100) with a one-line justification. Use these scoring weights: pain severity 30%, budget clarity 25%, timeline 20%, decision-maker involvement 15%, competition risk 10%. Notes: “[PASTE NOTES HERE]””

Metrics to track

• Average qualification score by week.
• Conversion rate: discovery → proposal for scores ≥75 vs <75.
• Time per note (before vs after).
• Human edit rate (percent of AI outputs changed).

Common mistakes & fixes

• GIGO: clean transcripts (trim small talk). Fix: use a 30‑second pre-clean checklist before pasting.
• Overtrusting score: use as decision support. Fix: require a one-sentence human verification for scores >=90 or <=30.
• Changing templates too often: lock one template for 2–4 weeks to establish baseline metrics.

1. One-week action plan
   1. Day 1: Run the prompt on 3 recent calls; record scores.
   2. Day 2–3: Compare AI outputs to your notes; tweak wording or weights once.
   3. Day 4–5: Have one teammate adopt the process for 5 live calls.
   4. Day 6–7: Review metrics (score distribution, time saved, edit rate) and set your operational thresholds (e.g., 75).

Your move.

[Steve Side Hustler]

Nice point — that quick win is exactly the confidence-builder folks over 40 need. If you can get a one-line summary and a 0–100 score in under five minutes, you already have more predictability than most teams. Here’s a short, practical micro-workflow you can use immediately that keeps things non-technical and low-friction.

What you’ll need

• Call transcript or clean bullet notes (typed within an hour).
• An AI chat box or the transcription tool you already use.
• A single, saved template in your CRM or a shared doc (same fields every time).

How to do it — step-by-step (under 5–10 minutes)

1. Paste cleaned notes into your AI tool. Trim small talk first — 30 seconds.
2. Ask the AI for a structured record with these fields: one-line summary, 3 key pain points, budget (Low/Medium/High/Unknown), decision timeline, named decision makers, competitors, suggested next steps, and a 0–100 qualification score with a one-line rationale. Mention the scoring priorities you care about (example weights below).
3. Scan the AI output and do a one-line human check: change the score or a field only if it feels clearly off. That keeps speed high and accuracy reasonable.
4. Paste the structured fields into your CRM or shared sheet. Use a simple rule: score ≥75 → propose, 50–74 → nurture, <50 → disqualify/revisit.
5. At week’s end, review 8–10 scored calls and note any consistent mismatches between AI and reality. Tweak weights or the template once and lock it for two weeks.

Suggested scoring priorities (quick guidance)

• Pain severity ~30%, budget clarity ~25%, timeline ~20%, decision-maker involvement ~15%, competition risk ~10%. Use these as a starting point and adjust based on your sales cycle.

What to expect

• Initial time: ~8–15 minutes per note; drops to 3–5 minutes after a few reps.
• Immediate benefits: faster triage, clearer next steps, fewer cold follow-ups.
• Keep the AI score as decision-support — require a one-line human confirmation for extreme scores (≥90 or ≤30).

Micro-habit to start today: run this flow on your next 3 calls. Don’t change anything until you see patterns — small consistent tweaks beat big overhauls.

[Jeff Bullas]

Turn every discovery call into a 5-minute scorecard you can trust. One template, one prompt, repeat. That’s how you get faster follow-ups, cleaner forecasts, and fewer “stuck” deals.

Do / Do not (quick checklist)

• Do lock one template for 2–4 weeks before changing anything.
• Do trim small talk and copy only the meaty parts of the notes.
• Do ask the AI for evidence lines and “missing info” questions.
• Do set score thresholds (≥75 propose, 50–74 nurture, <50 disqualify/revisit).
• Do add a one-line human check for extreme scores (≥90 or ≤30).
• Don’t let the AI guess names, budgets, or timelines—return “Unknown” if not stated.
• Don’t keep tweaking weights daily—review weekly.
• Don’t paste entire transcripts—cut to pain, money, timeline, decision-maker, risks.

What you’ll need

• Cleaned call notes or transcript snippet (5–10 key paragraphs or bullet points).
• Any AI chat you already use.
• Your CRM fields or a shared doc with the same field names every time.

Step-by-step (simple and repeatable)

1. Right after the call (within 60 minutes), paste cleaned notes into your AI chat.
2. Run the prompt below. It returns a clear summary, score, evidence lines, and next steps.
3. Scan for 30–60 seconds. If a field looks off, edit once. Add a one-line human confirmation on extremes.
4. Paste the fields into your CRM. Apply your threshold rule and act immediately.
5. End of week: review 8–10 scored calls vs outcomes. Adjust weights once, then lock for two weeks.

Premium trick (insider): score anchoring + evidence lines

• Add 2–3 tiny “example deals” and their scores inside the prompt. This anchors the AI’s scoring to your reality.
• Require 2–3 verbatim lines from your notes that drove the score. This kills hallucination and speeds your review.

Copy‑paste AI prompt (use as-is)

“You are a sales ops assistant. Convert discovery call notes into a structured record and an objective score. Use only what’s in the notes—if info is missing, return ‘Unknown’. Return the following fields: (1) one_sentence_summary, (2) pain_points (3 bullets), (3) impact_signal (what it costs them or delays, if present), (4) budget_estimate (Low/Medium/High/Unknown), (5) decision_timeline (Immediate/1–3 months/3–6 months/6+ months/Unknown), (6) decision_makers (names/roles if stated; else Unknown), (7) competitors_mentioned, (8) risks_or_red_flags, (9) next_steps (2–3 bullets), (10) qualification_score (0–100) with a one-line justification, (11) confidence (High/Medium/Low), (12) evidence_lines (2–3 verbatim lines from the notes), (13) missing_info_questions (3 short questions to close gaps). Scoring weights: pain severity 30%, budget clarity 25%, decision timeline 20%, decision-maker involvement 15%, competition risk 10% (higher risk lowers score). Guardrails: do not infer; if not explicit, return ‘Unknown’. Calibration examples: Example A (Score 88): Strong pain with quantified impact, clear budget range, timeline <90 days, decision-maker present, low competition. Example B (Score 65): Clear pain, budget unclear, 3–6 month timeline, influencer not DM, one competitor. Example C (Score 35): Vague pain, no budget, 6+ months, no DM, active incumbent. Now analyze these notes: [PASTE NOTES HERE]”

Worked example

Sample notes you might paste: “Acme Mfg (250 employees). ERP outages ~8 hrs/month; estimate $15–20k loss per outage. Current vendor: ‘homegrown system’ + manual spreadsheets. Considering Vendor X. Budget ‘approved up to 60–80k if ROI clear’. Decision-makers: CFO (Sara) + Ops Director (Luis). Timeline: target Q2 go-live; want pilot in 8–10 weeks. Needs: reduce downtime, inventory accuracy, light integrations to QuickBooks. Risks: IT bandwidth thin; CFO wants 3 references. Next step: send ROI case study and schedule pilot scope call next Tuesday.”

• Summary: Mid-sized manufacturer with costly ERP downtime seeks pilot in 8–10 weeks; budget likely sufficient if ROI proven.
• Pain points: Downtime losses; manual spreadsheets; inventory inaccuracies.
• Impact signal: ~$15–20k per outage; recurring monthly.
• Budget: High
• Timeline: 1–3 months
• Decision-makers: CFO (Sara), Ops Director (Luis)
• Competitors: Vendor X
• Risks: IT capacity; reference requirement
• Next steps: Send ROI case; book pilot scope; prep two relevant references
• Score: 82/100 — strong pain + budget + short timeline + DMs engaged; moderate competition and IT risk
• Confidence: High
• Evidence lines: “outages ~8 hrs/month…$15–20k loss”; “approved up to 60–80k”; “pilot in 8–10 weeks”
• Missing info questions: Who signs the contract? What integration scope is must-have? What success metric ends the pilot?

Common mistakes & quick fixes

• Messy input: Remove pleasantries, jokes, and unrelated side stories. Keep the buyer’s words on pain, money, time, people.
• Score drift: If average scores creep up or down week to week, re-run the same 3 calibration examples in your prompt.
• Invisible risks: Add a field for “risks_or_red_flags” so they don’t get buried in the summary.
• Inconsistent next steps: Ask the AI for 2–3 concrete actions with owners and timing; keep them short and specific.

What to expect

• Time per note: 8–15 minutes at first; down to 3–5 minutes once the template is muscle memory.
• Decision clarity: you’ll triage calls in seconds and stop over-nurturing low-fit deals.
• Quality control: evidence lines make review fast and reduce edits.

One-week action plan

1. Today: Paste your last two calls into the prompt. Save outputs in your CRM under a “Discovery (AI)” section.
2. Tomorrow: Add your 3 calibration examples to the prompt (one high, one mid, one low-fit).
3. Midweek: Run 5 live calls through the flow; apply the 75/50 thresholds.
4. End of week: Compare scores vs outcomes. Adjust one thing only (weights or threshold). Lock for two weeks.

Final nudge: Consistency beats cleverness. One template. One prompt. Five minutes after every call. That’s the system that compounds.

[Author]

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